class_docstr: One-layer ensemble
  
  An ensemble of estimators and preprocessing pipelines in one layer. Takes
  an input X and generates an output P. Assumes all preprocessing pipelines
  and all estimators are independent.
  
  Parameters
  ----------
  groups: list,
      list of :class:`Group` instances to build layer with.
  
  proba: bool (default = False)
      whether to call `predict_proba` on the estimators in the layer when
      predicting.
  
  propagate_features: list, range, optional
      Features to propagate from the input array to the output array.
      Carries input features to the output of the layer, useful for
      propagating original data through several stacked layers. Propagated
      features are stored in the left-most columns.
  
  verbose: int or bool (default = False)
      level of verbosity.
  
          - ``verbose = 0`` silent (same as ``verbose = False``)
  
          - ``verbose = 1`` messages at start and finish               (same as ``verbose = True``)
  
          - ``verbose = 2`` messages for preprocessing and estimators
  
          - ``verbose = 3`` messages for completed job
  
      If ``verbose >= 10`` prints to ``sys.stderr``, else ``sys.stdout``.
  
  shuffle: bool (default = False)
      Whether to shuffle data before fitting layer.
  
  random_state: obj, int, optional
      Random seed number to use for shuffling inputs
code: def get_params(self, deep=True):
      out = super(LayerEnsemble, self).get_params(deep=deep)
      if not deep:
          return out
  
      for group in self.groups:
          for g in group:
              for key, value in g.get_params(deep=deep).items():
                  out["%s__%s" % (g.name, key)] = value
                  out[g.name] = g
      return out
  
imports: from ..parallel import Layer, make_group
  from ..parallel.base import ParamMixin
  from ..parallel.wrapper import EstimatorMixin
  from ..parallel._base_functions import check_stack
  from ..externals.sklearn.base import BaseEstimator
  from ..externals.sklearn.base import clone
label_desc: Use image node_id 5 for calling the LayerEnsemble obj's underlying member method code with example usage: obj.get_params(deep) and returns: out, out